The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed from modern machines such as Medical Resonance Imaging (MRI) scanners, Computed Tomographic (CT) scanners, and Positron Emission Tomographic (PET) scanners, to multimodality imaging systems such as PET-CT and PET-MRI systems. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor in order to reach an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
CAD systems may be used to process images acquired by PET imaging systems. PET a functional imaging technique that produces a three-dimensional image of functional processes in the body. As PET data is acquired, attenuation of some of the PET photons may occur. Attenuation can lead to degraded image quality and reduced quantitative accuracy. Accordingly, in certain situations, such as patient imaging, PET imaging is combined with X-ray computed tomography (CT) imaging to correct for such attenuation. X-rays from a CT scan may be used to provide information directly related to attenuation coefficients of the material being imaged.
However, PET-CT imaging has some limitations. One disadvantage is that CT provides only limited soft tissue contrast and exposes the imaged subject to significant radiation dose. To overcome these limitations, recent work has combined PET with MRI. PET-MRI reduces the amount of radiation exposure to the subject, and has shown very promising results, including excellent soft tissue contrast. Unlike CT data, however, MRI data does not directly relate to the attenuation of photons. It may be possible to generate an attenuation map based on MRI data by producing a tissue classification or pseudo-CT image from which an attenuation map can be produced.
Segmentation of MRI data is crucial for attenuation correction of PET imaging, as well as other tasks such as automated bone metastasis detection. Once structures (e.g., bone) are detected and segmented, known attenuation values may be used to generate an attenuation map for PET image reconstruction. One problem with segmenting structures such as the cortical bone, however, is that it produces no signal in MRI, and is therefore ambiguous with air. This makes primitive detection and segmentation algorithms inaccurate, and therefore insufficient for the task.
Therefore, there is a need for improved systems and methods for segmentation in medical imaging.